Add parameter to decide on whether to check for node purity or not

src/main/java/ca/joeltherrien/randomforest/Settings.java

@@ -177,6 +177,7 @@ public class Settings {
     private int numberOfSplits = 5;
     private int nodeSize = 5;
     private int maxNodeDepth = 1000000; // basically no maxNodeDepth
+    private boolean checkNodePurity = false;
 
     private ObjectNode responseCombinerSettings = new ObjectNode(JsonNodeFactory.instance);
     private ObjectNode groupDifferentiatorSettings = new ObjectNode(JsonNodeFactory.instance);

src/main/java/ca/joeltherrien/randomforest/tree/TreeTrainer.java

@@ -27,6 +27,12 @@ public class TreeTrainer<Y, O> {
     private final int maxNodeDepth;
     private final int mtry;
 
+    /**
+     * Whether to check if a node is pure or not when deciding to split. Splitting on a pure node won't change predictive accuracy,
+     * but (depending on conditions) may hurt performance.
+     */
+    private final boolean checkNodePurity;
+
     private final List<Covariate> covariates;
 
     public TreeTrainer(final Settings settings, final List<Covariate> covariates){
@@ -34,6 +40,7 @@ public class TreeTrainer<Y, O> {
         this.nodeSize = settings.getNodeSize();
         this.maxNodeDepth = settings.getMaxNodeDepth();
         this.mtry = settings.getMtry();
+        this.checkNodePurity = settings.isCheckNodePurity();
 
         this.responseCombiner = settings.getResponseCombiner();
         this.groupDifferentiator = settings.getGroupDifferentiator();
@@ -48,7 +55,7 @@ public class TreeTrainer<Y, O> {
     }
 
     private Node<O> growNode(List<Row<Y>> data, int depth){
-        // TODO; what is minimum per tree?
+        // See https://kogalur.github.io/randomForestSRC/theory.html#section3.1 (near bottom)
         if(data.size() >= 2*nodeSize && depth < maxNodeDepth && !nodeIsPure(data)){
             final List<Covariate> covariatesToTry = selectCovariates(this.mtry);
             final SplitRuleAndSplit bestSplitRuleAndSplit = findBestSplitRule(data, covariatesToTry);
@@ -161,8 +168,22 @@ public class TreeTrainer<Y, O> {
     }
 
     private boolean nodeIsPure(List<Row<Y>> data){
+        if(!checkNodePurity){
+            return false;
+        }
+
+        if(data.size() <= 1){
+            return true;
+        }
+
         final Y first = data.get(0).getResponse();
-        return data.stream().allMatch(row -> row.getResponse().equals(first));
+        for(int i = 1; i< data.size(); i++){
+            if(!data.get(i).getResponse().equals(first)){
+                return false;
+            }
+        }
+
+        return true;
     }
 
     private class SplitRuleAndSplit{